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TRADITIONAL AND CHALLENGER BANKS IN UK:
COMPARISON IN TERMS OF CUSTOMER VALUE
Sara Santos
Dissertation written under the supervision of João Freire de Andrade
Dissertation submitted in partial fulfilment of requirements for the
MSc in Finance, at Universidade Católica Portuguesa,
September 2018
i
ABSTRACT
Title of the Thesis:
Traditional and Challenger banks in UK: Comparison in terms of customer value
Author: Sara Maria Sustelo Santos
Keywords: FinTech, Banks, Customer Value, Customer Experience
Banking is about delivering value to the customer. There are two main type of banks:
Traditional banks with centuries of history, a large customer base, a trusted, familiar and
recognized brand, which have been challenged by newcomers – the Challenger banks as they
started to rethink customer journey within banking business models. The purpose of this
research is to conclude on which type of bank provides more value to their customers, which
factors contribute for that and study the effect of those results on consumer behavior outcomes,
in UK. The hypotheses are formulated after an extensive review of both academic literature and
white papers about FinTech, banking industry and customer value measurement methods.
Using primary data methods obtained with 201 valid respondents from UK, contacted online,
performing scale development, CFA and SEM, resulted in the development of a customer value
measurement method with 34-items and eight factors. In general, the results showed that
Challenger banks provide higher customer value than Traditional banks, presenting higher
average scores in seven factor while Traditional banks stand-out in one factor. Moreover,
customer value and its factors predict consumer behavior outcomes Word-of-mouth and
Loyalty, being the factors Price offer fairness and Outcome focus highly relevant because of its
close link to the outcomes. The research contributes to existent academic literature on FinTech,
banking industry with a validated customer value measurement method, having also practical
implications for managers of financial institutions, giving them a useful tool for the
development of specific strategies of marketing.
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ABSTRACT (Portuguese version)
Título:
Traditional and Challenger banks in UK: Comparison in terms of customer value
Autor: Sara Maria Sustelo Santos
Palavras-chave: FinTech, Bancos, Valor para o cliente, Valor
O setor bancário está relacionado com a entrega de valor ao cliente. Existem dois tipos
principais de bancos: os Tradicionais, com história, elevado número de clientes, marca de
confiança, familiar e reconhecida, que têm sido desafios pelos bancos conhecidos por
“Challengers”, que repensaram a experiência do cliente e o “business model” do setor bancário.
O objetivo desta pesquisa é concluir que tipo de banco fornece mais valor ao cliente, quais os
fatores que contribuem para tal e estudar o efeito desses no comportamento do cliente, no Reino
Unido. As hipóteses são formuladas após uma extensa revisão da literatura académica,
relatórios sobre “FinTech”, o setor bancário e medidas de valor. Dados primários foram obtidos
por contacto online alcançando 201 indivíduos, clientes de bancos do Reino Unido. Utilizando
CFA e SEM, resultou num método final com 34 itens e oito fatores que o valor. No geral, os
resultados demonstram que os bancos “Challenger” fornecem mais valor ao cliente do que os
Tradicionais, apresentando uma pontuação mais elevada em sete fatores enquanto os
Tradicionais apresentam maior num fator. Para além disso, o valor para o cliente e os seus
fatores explicam os comportamentos do cliente “Word-of-mouth” e “Loyalty”, sendo que os
fatores “Offer fairness” e “Outcome focus” são relevantes pelo seu poder explicativo dos
comportamentos. A pesquisa contribui para literatura existente sobre “FinTech” e o setor
bancário, com um método válido para medir valor, tendo implicações práticas para gestores de
instituições financeiras, com uma ferramenta útil para delinear estratégias de marketing.
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TABLE OF CONTENTS
1. INTRODUCTION ........................................................................................................................... 1
2. LITERATURE REVIEW ................................................................................................................ 3
2.1. FinTech in Banking Industry ................................................................................................... 3
2.2. Traditional banks versus Challenger banks ............................................................................. 5
2.3. Customer Value ....................................................................................................................... 9
2.4. Consumer Behavior ............................................................................................................... 12
2.5. Research Hypotheses ............................................................................................................. 13
3. METHODOLOGY ........................................................................................................................ 15
3.1. Methodology synthesis .......................................................................................................... 15
3.2. Survey sampling .................................................................................................................... 15
3.3. Data collection ....................................................................................................................... 17
3.4. Descriptive statistics of Data ................................................................................................. 18
3.5. Scale development and validation ......................................................................................... 19
3.6. Measurement models ............................................................................................................. 23
4. RESULTS ...................................................................................................................................... 28
4.1. Results from the first study ................................................................................................... 28
4.1.1. Scale validation ............................................................................................................. 28
4.1.2. Descriptive statistics of items ........................................................................................ 30
4.1.3. Estimated factor scores (model 1) ................................................................................. 32
4.1.4. Overall customer value score (model 2) ........................................................................ 33
4.2. Results from the second study ............................................................................................... 33
4.2.1. Models validation .......................................................................................................... 33
4.2.2. Descriptive statistics of items ........................................................................................ 34
4.2.3. Estimated factor scores: WOM and LOY (models 3.1 and 3.2) .................................... 35
4.2.4. Effects on consumer behavior outcomes (model 4 and model 5) .................................. 36
5. CONCLUSION ............................................................................................................................. 38
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TABLE OF FIGURES
Figure 1 - Explaining customer behavior (adapted from Klaus, 2015) ................................... 12
Figure 2 – Second study SEM conceptualization . .................................................................. 14
Figure 3 - Selected Articles of Previous Studies for this dissertations ................................... 15
Figure 4 - Completion Rates and Missing Data explanation ................................................... 16
Figure 5 - Number of respondents for each study divided in three different possibilities ...... 16
Figure 6- Descriptive Statistics: Demographic Data of respondents ...................................... 18
Figure 7 – Scale development ................................................................................................. 19
Figure 8 - Internal Consistency of measurements with dataset of Pilot-testing ...................... 20
Figure 9 – Internal Consistency of measurements with final dataset ...................................... 22
Figure 10 – Word-of-Mouth (WoM) and Loyalty Behavior (LOY) factors ........................... 25
Figure 11 – Structural Model 4 ............................................................................................... 27
Figure 12 - Structural Model 5 ................................................................................................ 27
Figure 13 – Results of CFA for the 1st order 8-factor model (with p-value = 0.00 < 0.05) .... 29
Figure 14 – Means and standard deviations of items .............................................................. 32
Figure 15 – Descriptive statistics 1st order 8-factor model ...................................................... 32
Figure 16- Customer value overall score ................................................................................ 33
Figure 17 – CFA results of WOM and LOY measurement models 3.1 and 3.2 .................... 34
Figure 18 – Means and standard deviations for items part of WOM and LOY ...................... 35
Figure 19 – Descriptive statistics of models 3.1. and 3.2 ........................................................ 35
Figure 20 – Results from SEM of model 4 ............................................................................. 36
Figure 21 – Results from SEM of model 5 ............................................................................. 37
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APPENDIX
Appendix 1 – Table with areas of activity of FinTech start-ups ……………………...……….40
Appendix 2 – Service experience construct and measure model (EXQ)………………...……41
Appendix 3 – Neobanks and Challenger banks in UK per category………………………..…42
Appendix 4 – Distribution of Traditional and Challenger banks in the sample………….……43
Appendix 5 – Questionnaire structure…………………………………………………...……44
Appendix 6 – Questions part of the survey with each respective item…………………….…..45
1
1. INTRODUCTION
FinTech or Financial Technology is one of the most promising industries in 2016 (Chishti and
Barberis, 2016). The financial industry has been going through a lot of change, FinTech
Startups are entering the market offering not new, but revolutionized services that are
traditionally offered by established financial institutions, such as banks and insurance
companies (Dapp, 2014). However, this industry is one of the last large industries that has not
been completely disrupted by the digital revolution. Most banks in the financial sector have a
conservative attitude towards solutions and think that the heavy regulation will protect them
and limit the enter and growth of FinTech Startups not understanding that tech companies are
agile enough to make use of the existing regulation (Gelis, 2016). FinTech Startups try to solve
gaps in the customer journey. Successfully, many of them already reached a critical mass of
users proving the viability of their business models. Revolut, a digital banking alternative, broke
even in December of 2017 for the first time and claims to have reached 2 million users (CNBC,
June 2018) with its $1.7 billion post-money valuation in Series D funding (TechCrunch, April
2018). FinTech Startups usually focus on a very specific niche segment of the industry while
banks try to compete on all levels possessing every aspect of the financial services range
(Rachel Nienaber, The FinTech Book, pp. 21, 2016). Almost every financial service that a bank
offers is also offered, or soon will be, by a FinTech company (Appendix 1). In the past, banks
were the only option for the costumer’ financial needs and this last for a long time. Today,
there’s an alternative to banks services. While other industries were being disrupted, it took
more time for that to happen in Finance sector. According to TransferWise1 Report (“Future
of Finance”, 2016), in ten years the financial services sector will be transformed and the main
driver for that to happen is behavior and expectations of customers. In the same report,
TransferWise presents five conditions that allow the FinTech Startups to enter the sector: loss
of trust in banking sector after the global financial crisis of 2008, following that the expectations
of customers are higher, the rise of millennials and of the mobile internet and finally, changes
in regulation that focus more on the rights of the customer (PSD2, GDPR)2. Fasnacht (2009)
argues that changes in customer demographics and their requirements affected Financial
Services conservative industry bringing more innovation and new business opportunities.
1 UK-based money transfer service launched in January 2011. 2 PSD2: Payment Services Directive (European Commission Press Release, October 2015). GDPR: General Data
Protection Regulation
2
In the past, customers valued convenience and visibility so that banks who had larger branch
networks would standout, competing on product, price and scale regarding the number of
branches they had. Today, the main competitive factor is customer experience, that combines
what is sold and how that is delivered, being both critical components of the customer journey
(EY, 2017). Most of bank customers now prefer to access their financial information through
mobile banking and, as a response to that, Banks have been closing branches since the financial
crisis of 2008. For example, in UK more than 1,000 banks branches have closed between 2015
and 2016 (Dunkley, 2016). Furthermore, according to research from CACI3, consumer visits to
retail bank branches will decline 36% between 2017 and 2022, while mobile transactions will
rise 121% in the same period. Closing branches reduces operation costs for being the average
cost saving around £200.000 annually according to Deloitte (2014). Additionally, according to
Accenture (North America Consumer Digital Banking Survey, 2015), 81% of customers would
not change banks if their local branch closed. EY Global Consumer Banking Survey (2017)
confirms that banks are under pressure to master the customer experience due to two reasons:
increasing commoditization, i.e., customers don’t see differences between Traditional banks
offering and business models, and new competition from FinTech Startups and other new
market entrants that give more importance to customer value. Furthermore, it is estimated that
80% of sold devices by 2020 will be smartphones and that mobile data consumption will
increase seven-fold by 2021 (Cisco Mobile Visual Networking Index Forecast, 2016). In this
new setting, banks should focus on offering products that are simple, visual and user-friendly
having the customer relationship as a focus (Erman, 2017).
Until now there is no research that aims to assess the customer value that Challenger banks
provide and compare it with Traditional banks, in order to understand their main differences
when relating to customers. This research focuses on two studies. First, the comparison, in
terms of customer value and its factors, between Traditional banks and Challenger banks, with
a scale development and an assessment of customer value, to conclude about which group offer
more value to their customers and which drivers are behind that. The second study, is about the
effect of customer value from both type of banks, on consumer behavior outcomes such as
Word-of-Mouth (WOM) and Behavioral Loyalty Intentions (LOY).
3 Consolidated Analysis Centers, Inc.
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2. LITERATURE REVIEW
2.1.FinTech in Banking Industry
The inception of the credit card in the 1950s and ATMS in the 1970s changed the way people
access and pay for goods. The internet revolution in the early 1990s had a profound impact in
the financial markets worldwide, having emerged many e-finance business models such as
online banking, online brokerage services, mobile banking and payments. The FinTech
revolution has been building a new appearance of the financial world after the global financial
crisis in 2008, according to The Economist (2015), and the most beneficiary from its fast growth
are the consumers (Rometty, 2016).
FinTech is still at its beginning, however, has been gaining popularity not only in financial
markets but also in research. The few scientific researches about FinTech are about the effects
that the FinTech Startups have on established players in the financial markets or about the
business areas that these types of companies develop, and how they interact with each other
(Stuckenborg et al., 2017).
There is a broad agreement between scientific and practical experts that FinTech is a
combination of the words “financial” and “technology”. It can be simply described as “the use
of technology to deliver financial solutions” (Arner et al., 2015, p.3). For the research purpose
of this dissertation, it will be considered the definition given by Sia et al. (2016, p. 105) who
describes FinTech as “a new generation of financial technology Startups that are
revolutionizing the financial industry”. In total, it’s distinguished twelve different areas of
FinTech activities, presented in Appendix 2, with its area of activity, description and an
example of a FinTech Startup. For every financial service virtually, there’s a FinTech Startup
(Chen, 2016). In this research, the FinTech Startups considered include Neobanks and
Challenger banks, excluding all the other type of FinTech activities.
According to BBVA, there are two main groups of FinTech banks: Neobanks and Challenger
banks. Neobanks are an internet/mobile bank that offer more customized services focus on a
niche of the market, having as main value proposition the user interface/experience, however,
it needs to have a partner bank, i.e., they rely on a real bank’s infrastructure so that they work
as an interface. Challenger banks offer very similar services as a Traditional bank with lower
costs since they build their own infrastructure from scratch, i.e., they do not rely on another
bank, having themselves a banking license or are in the process of getting one and, they don’t
4
have legacy costs so that is easier to get market share. This type of banks aim to become all-in
banks, but without branch-based distribution channels being mobile banks (BBVA, 2016).
Challengers banks and Neobanks have as main differences the banking license and the full
control on the core banking system, having Challenger banks more ability to innovate according
to customer’s needs since they don’t rely completely on third party providers (Djelassi, 2017).
The main challenges for Neobanks are (1) the cost of customer acquisition and (2) the
dependence on a partner bank while for Challenger banks is the first one (Trieu, 2015). Another
possible classification is GAFA banks, which are banks that would exist if a Tech giant such as
Google or Facebook, created a bank (Barberis, 2016).
Neobanks and Challenger banks can have a Business-to-Consumer (B2C), a Business-to-
Business (B2B) type of business or both. In the B2C type of business, the bank offers its
products and services directly to the consumers (E.g. Mondo, N26)4. In the B2B type of
business, the bank sells its services to other business such as Small or Medium Enterprise
Business (SMEs) or sole traders (E.g. Tide, Counting up)5. Other digital banks such as Starling
Bank and Revolut6 are focused on both type of businesses. Research by Burnmark (2016) shows
that 43% of Challenger banks in the world offer only basic products such as current accounts
and saving accounts. The other 57% offer Traditional products such as mortgages, SME
lending, children’s savings and insurance. For Challenger banks the significant source of
revenue are not large organizations but individual customers and SMEs (Lee et al., 2018).
The last type of bank presented is the one that exists for longer and that it’s often associated
with the concept “bank”. In this dissertation, this type of bank will be called Traditional banks.
Banks are “institutions whose current operations consist in granting loans and receiving
deposits from the public” (Freixas and Rochet, 1997). These represents banks’ core activity that
distinguishes them from the other financial institutions. Roengpitya et al. (2014) classified three
banks business models through a statistical clustering algorithm using data from balance sheet:
retail-funded, wholesale-funded and trading banks. The first business model that was label
commercial “retail-funded”, it’s characterized by having a high share of loans in their balance
sheet and a high reliance on stable funding sources such as deposits. Fasnacht (2009) classifies
4 Mondo is a mobile first bank. N26 is challenger bank. 5 Tide is a neobank that offers a current account for SMEs. Counting up is an accounting bank. 6 Starling Bank focuses on helping users manage their money. Revolut is a current account in a smartphone.
5
retail banking as a service for end users, distinct from commercial banking that is focused on
companies.
In this dissertation, Traditional banks definition is in accordance with the previous definitions
of Retail Banking and “retail-funded”, added the fact that will only be considered established
financial institutions with more than five years of existence that have physical branches and
offer at least the following services to their customers: credit, deposit and money management.
For the purpose only Neobanks, Challenger banks and Traditional banks are considered. The
focus of the dissertation is the comparison, in terms of customer value, between Traditional
banks and a new type of banks that joins Neobanks and Challenger banks and will be aggregated
and called Challenger banks. Furthermore, the focus is on consumer banking, i.e., B2C type of
business and for that reason, banks that only do B2B will not be considered. Moreover,
consumer banking is the most likely to be disrupted by FinTech according to 73% of the
financial sector executives (PWC, 2016).
2.2.Traditional banks versus Challenger banks
FinTech Startups are disrupting the existing products and services, with a focus on user
experience, extracting value from data, decreasing operation costs and increasing efficiency
with their business models, through advance technology (Chappuis Halder, 2015). As
newcomers, Challenger banks can rethink the banking business model and the technology
behind it. However, barriers to entry remain high and it takes time to build a recognized,
familiar and trusted brand such as Traditional banks have (Djelassi, 2017). Chuen and Teo
(2015) identified the LASIC principles that new disruptive businesses should aim to have
success, being this not sufficient, but necessary conditions: low margin, asset light, scalable,
innovative and compliance easy business models. Traditional banks and Challenger banks have
different capabilities which lead to different strategies in the financial sector. Consequently,
understanding their core points of differentiation is useful for this research.
Osterwalder, Pigneur et al. (2010) propose for companies to build business models based on
the customer perspective. Being an innovative and customer-centric company is vital for
survival and growth (Capgemini Worldwide, “World FinTech Report”, 2018). Around 53% of
the Traditional banks say they are customer-centric while for FinTech Startups that accounts
for 80% (PWC, 2016). FinTech Startups seem to follow this customer-centric approach, since
they are able to understand their customers better than the Traditional banks and thus address
their needs in a more effective way (Mackenzie, 2015). DBS Bank’s Sonia Wedrychowic, Head
6
of Consumer Bank Technology Singapore, advocates for Traditional banks to assume an
outside-in perspective, keeping the customer journey in mind in a way that the customers design
the customer experience instead of looking at the journey from the bank’s perspective. In most
cases, Challenger banks provide a more efficient way to sell the same old products and services,
possibly because of the technologies used, but in a different and unbundled way. These
unbundled activities have limited scope. However, this ability of unbundle services has been
very disruptive for Traditional banks (Walchek, 2015) and one of the major drivers of growth
in the FinTech sector (Lee et al., 2018). Offering personalized niche services and providing
services that are more personalized and more segmented to the customer’ needs, has been one
of the key differentiations of Challenger banks. Although Traditional banks are in disadvantage
regarding the unbundling of services, since they provide one-stop comprehensive financial
services and products to customers, being their value chain based on many bundled activities,
this provides them powerful economies of scope (Navaretti et al., 2017). According to KPMG
(“Banking the Customer Experience Dividend”, 2016) most of Traditional banks’ relationships
with their customers have become standardized having a lack of emotional differentiation that
reinforces unexploited financial opportunity. Regarding channels as a distribution element it’s
important to compare Traditional and digital banking models. Consistency in customer
experience is the focus for digital banking model since channels are non-existent, contrarily of
the Traditional model that has an inconsistent customer experience across channels
(Padmaavathy and Adalarasu, 2015). Traditional banks have used their branches to acquire
customers. On an average, customers go to a branch once or twice a year and with their mobile
devices they interact 20 to 30 times per month, according to Luvleen Sidhu, President of
BankMobile (2018). Traditional banks have been through a lot of change in the past years,
specially related with the online banking systems, however they’re doing it at a slow pace.
Before, most of the transactions required the presence of the customer in the branch (Landers,
2016) which took lots of time and effort, compared with online banking. Staff levels have been
reduced, the most unprofitable branches have been closed and new branch concepts start to
being tested. PWC (“Retail Banking 2020: Evolution or Revolution”, 2014) expect these trends
to speed up. Furthermore, Traditional banks are providing better user experience, being their
services faster and more user-friendly than they were some years ago. Although most of the
largest banks have initiated their online services and closing of branches, Challenger banks
already surpass the digital banking model itself. Challenger banks stand-out due to extreme
minimalism in design and functionality, simplification, easiness in use and on the eyes
reflecting core User Experience (UX) principles (Chen, 2016) which is reflected in the design
7
of the app from the customer’s perspective. Besides working only in online and mobile context,
Challenger banks have less complex IT systems, simpler product set, more streamlined and
automated operating models and fewer legacy compliance issues (KPMG, “Challenger banking
report” 2016) compared with Traditional banks that have increasing costs due to more austere
banking regulatory environment. While Traditional financial institutions are working on their
business models trying to optimize them, they are surround by immense regulation burdens.
Nevertheless, Traditional banks have as advantage, knowledge about existing regulation and
the ability to forecast the evolution of the industry (Philippon, 2016) while most Challenger
banks do not have the expertise to understand and comply with the new regulations. However,
they are not subject to high compliance regimes which encourages them to be more innovative
and entails lower capital requirements. Douglas (2016) wrote that the success of FinTech
Startups depends on combining their cutting-edge technology capabilities and flexibility in
changing regulations.
One of the key advantages of Traditional banks is their huge customer base (Philippon, 2016)
and the time-honored relationships built with their customers, which represents a unique
opportunity to use big data techniques to provide a personalized. Personalization is needed to
drive growth and shareholder value and accounts for 23% of the overall customer experience
(KPMG, 2016). It seems that Challenger banks are doing a good job in terms of customer
acquisition because of the great user interface they are offering and the focus on customer
experience, however, Traditional banks have been much more experienced and good at it
because “of the stickiness of the direct deposit checking account relationship”, according to
Luvleen Sidhu (2018). Traditional banks have made it hard for customer to switch from one
bank to the other: banks try to attract customer as early as possible and they use long term
products such as mortgages or loans to lock customers as long as possible (Djelassi, 2017).
Challenger banks will have to maintain their customer acquisition cost (CAC) as low as possible
and “manage their profitability/growth dilemma until their business model becomes the new
standard of doing banking” according to Djelassi (2017). Traditional banks have established
technologies that are in the best case partly integrated after successive mergers that have left
banks like this (Kumar, 2016). Besides that, they have established processes, being the structure
and pace of transactions standardized and although they’re not fast as customers would like it
to be, they are predictable and familiar, since the bank behavior is rarely surprising, according
8
to Galarza (2017), founder and CEO at Entryless7. Challenger banks take the advantage of their
flexibility and agility due to freedom from legacy burdens to offer a new service or product that
matches with consumers’ new habits (Vauplane, 2015). FinTech Startups have a lean and agile
movement in the financial sector, starting with their culture and innovative business models
that are based on advanced technology such as Blockchain and cloud infrastructure that helps
enhance customer experience and reduce costs (Chishti and Barberis, 2016) or authentication
technologies that avoid the customer going to a branch (Burnmark, 2017). Traditional banks
are feeling the need to adapt to a new world. Investments in innovation programs and R&D of
new processes and technologies are happening. However, R&D is fundamentally different from
innovation and few banks provide more than sporadic disclosures regarding innovation and
mostly of the disclosures are qualitative (Larsen, 2017).
Challenger banks had the “first mover” advantage, however, they still lack scale in most of the
cases because Traditional banks are viewed positively by customers regarding trust. In
Burnmark research (“Challenger Bank battlefield”, 2017) it was found that 23.6% of customers
of FinTech Startups providers have trust in them compared with the 26.6% for Traditional
financial institutions. Furthermore, Traditional financial institutions have an advantage over
FinTech Startups regarding fraud protection, quality of service and transparency, according to
the same report. Also, Traditional banks have a strong market position in terms of security, trust
and antimoney-laundering aspects (Lukanova and Vasiljeva, 2016). In Accenture’s 2015 North
America Digital Banking Survey, Traditional banks were trusted by 86% of customers to
manage securely personal data compared with other institutions, which means that banks can
and should use this as an advantage when it comes to customer data. Nevertheless, transparency
is not completely supported by other studies as being one advantage of Traditional banks over
Challenger banks. A market investigation about retail banking in UK by CMA8 in 2015 revealed
that although banks advertise personal current accounts as free, those generate revenues of £8
billion per year, which customers pay in foreign transaction charges, overdraft charges and
foregone interest. Challenger banks claim to have a different approach regarding fees, being
transparency the main driver of their business. Moreover, Challenger banks have been able to
achieve cost optimization by using cost-effective methods. In a world of wide-spread internet
access, most services are free and users have low willingness to pay for those. This will translate
7 Entryless is a bill automation and payment platform 8 Competition & Markets Authority
9
into a period in which the initial margin will be low or inexistent but over time it increases, with
different sources of revenue being captured (Chuen and Teo, 2015).
2.3. Customer Value
“Banking is about attracting customers and making them feel good about their relationship with
the bank so that they become customers for life”, said Jay Sidhu, chairman and CEO of
Customers Bank.
In the banking industry the relationships between the institutions and the customer are focused
on the long-term in order to benefit from customers’ loyalty and participation (Berry, 1983).
Furthermore, keeping a customer in a continued relationship with the bank can be up to ten
times cheaper than attracting a new customer (Heskett et al., 1990). Value creation and
delivering to the customer is very important in the banking industry and the agents in this space
(banks) should be providers of value. Many changes have been happening in the last years in
these industry, being the customers’ behavior and expectations one of the main ones (Gardener
et al., 1999).
The literature on financial services specifies that one of the main fundamental points that banks
should be focused on, is customer perceived value (Marple and Zimmerman, 1999). First, it’s
important to know what is customer value, how customers form their valuations and how should
be measured. According to Payne et al. (1999) many banks use the term “customer value” to
refer to the value that the customer creates for them and not the value that they can deliver to
their customers. However, there is a consensus regarding seeing value as a customer perception
and cannot be determined by the provider of the service, being a subjective evaluation, i.e., for
different customers, the dimensions of perceived value might be differentially weighted
(Woodruff, 1997). Furthermore, value perceived by customers is not static, is a dynamic process
that changes over time (Hansen et al., 2013; Parasuraman and Grewal, 2000).
There is one conceptualization of customer perceived value, according to Sanchez-Fernandez
et al. (2006) that defines customer value as a multidimensional construct that has more than one
dimension being this approach the most appropriate for customer value in banking industry
since there are different elements that can potentially measure it: quantitative (price, cost
reduction, speed, time saved) or qualitative elements (newness, customization, design, customer
experience, user experience, brand/status, convenience).
10
Sheth et ak. (1991) identified five types of customer needs: functional value, social value,
emotional value, epistemic value and conditional value. Sweeney & Soutar (2001) included
factors to the functional value dimension (price or value-for-money, adaptability and quality of
the product) and claimed that neither conditional value nor epistemic value should be
considered for the customer value construct. Izquierdo et al. (2006) classified the dimensions
of customer value in three categories: functional value, affective value which includes both
social and emotional value and saving value. Furthermore Roig et al. (2006), adapting the
GLOVAL9 scale, found that customer value in the banking industry has six dimensions:
functional value divided in four dimensions regarding the installations of the bank, the
personnel, the service quality and the price, plus other two dimensions, social value and
emotional value, represented by a total of 22-items.
In general, the authors who treat customer value as multidimensional construct agree that two
main dimensions can be differentiated: a functional and an affective dimension. The first one
assumes that individuals make rational and economic valuations (Roig et al., 2006) while the
second one contemplates that “there are non-reasoned reactions that are formed in the
customer’s subconscious” according to Sanchez et al. (2006).
To measure customer value there are several methods available. The most known and simple
way to measure would be using the Net Promoter Score (NPS) created by Fred Reichheld
(2006) based on the perspective that customers of a company can be divided into three
categories (promoters, passives and detractors) and the difference between the percentage of
the two extreme groups represent the NPS. Traditional banks have open the market enter of
Challengers through persistently presenting low customer satisfaction, having the largest UK
banks an average NPS of -24 (Bernoff, 2011). Although NPS has great benefits with its
simplicity and ease of data collection, it does not generate the scientific data to identify which
factors influence most customer value, i.e., there’s a lack of ability to identify and correct the
drivers behind those methods (Klaus, 2015). Another method more focused on measure the
service quality is SERQUAL or its commercial equivalent “Rater”, which measure the gap
between customers’ expectations and customers’ perceptions of the service (Parasuraman et al.
1988). However, SERQUAL’s dimensions are too limited to capture the customer experience
completely (Sureshchangar et al., 2002). A need to measure customer experience (CX) before
and after the service encounter(s), considering both direct and indirect contacts and the social
9 A scale that measures the overall perceived value of a purchase.
11
context or peer influences (Berry et al., 2002; Payne et al. 2008), resulted in the creation of
EXQ - a measure of customer experience quality developed by Klaus and Maklan (2011). EXQ
represents a 19-items with four dimensions’ scale and besides explaining customer experience,
also predicts consumer behavior better than other methods such as customer satisfaction or NPS
(Klaus and Maklan, 2013). The service experience construct and measure (EXQ) is in Appendix
2.
Customer experience is linked to intentions and a customer’s state-of-mind (e.g. customer
satisfaction, loyalty or likelihood of a customer giving a recommendation) or actual behavior
(e.g. actively recommending the company’s services or products, purchase an item and
repurchase it or word-of-mouth behavior). The banking experience of the client is the sum of
all interactions that the customer perceives along the entire customer journey when interacting
with its bank (Gautam, 2017). In the Digital Banking Report’s Customer Experience in Banking
survey (2017) 90% of banks said that CX is a priority, and approximately three-quarters expect
to increase their investment in CX this year. However, only 37% of organizations have a formal
CX plan and the CX’s objectives at most financial institutions rely on internal benefits such as
more sales and cost cutting “to improve the share of wallet” (29%) and “to gain efficiency”
(25%), and not in customer benefits.
EXQ measures customer’s experience quality through four dimensions: Peace of mind (PEA),
Moments-of-truth (MOM), Outcome focus (OUT) and Product experience (PE), which captures
mostly the functional value having also emotional elements. Matzler (2006) argues that
companies should consider price satisfaction’ dimensions when monitoring customer
satisfaction and showed that five dimensions influence it being two of them Price Transparency
and Price fairness. Therefore, two other constructs to measure customer value regarding the
functional value dimension were considered for this dissertation: Price Transparency (Störmer,
2004) and Price offer Fairness (Herrmann, 2007). Störmer (2004) analyzed the effect of a cost-
based price presentation in customers’ satisfaction regarding factors such as price transparency,
WTP10, loyalty and purchase intentions, in a motor insurance context. The results showed that
an additional cost presentation significantly rises customers’ satisfaction, having a positive
consequence on their purchase decisions and their willingness to recommend the offer
purchased. The construct Satisfaction with perceived price transparency used in the study was
adapted for the present dissertation and will be referred as Price Transparency. Herrman (2007)
10 Willingness to Pay
12
studied how price fairness influences customer satisfaction and has demonstrated empirically
that this construct has a direct effect on satisfaction judgements and an indirect effect through
price procedure fairness, in the context of automobile purchases. The construct of Price Fairness
will be considered as a construct for this study.
Besides the functional value as a dimension for customer value, affective value will also be
considered as an important dimension of customer value. The affective dimension is divided
into two sub-dimensions: emotional and social (Izquierdo et al., 2006). The emotional
dimension is related with feelings or internal emotions generated by the experience, while the
social dimension is related with the social impact of using a product or service (Sánchez et al.,
2006). Emotions play an important and critical role in customer behavior (Klaus, 2015) being
crucial to include an emotional value dimension. Furthermore, an individual experience with a
company can be also dependent on the “social experience of a group or wider social context”
(Gentile et al., 2007). Social benefits that the customer receives from establish a relationship
with the bank are of great importance for customers (Peterson, 1995). Emotional and Social
value dimensions of customer value are adapted from Roig et al. (2006) and Ivanauskiene et al.
(2012).
2.4. Consumer Behavior
Besides the study of customer value, it’s important that the customer value is able to explain
consumer behavior outcomes being Behavioral Loyalty Intentions (Zeithmal et al., 1996;
Parasuraman et al., 2005) and Word-of-Mouth (WoM) Behavior (Brown et al., 2005) identified
as the most important outcomes of service quality in the literature (Anderson et al. 1994;
Verhoef et al., 2003, Dagger et al., 2007). This dissertation uses several methods to measure
customer value’ dimensions and to explain consumers’ behavior, such as Customer value will
measure the cause (customer value with all of its dimensions) in relation to the effects or
outcomes such as WoM Behavior and Behavioral Loyalty Intentions (Figure 1).
Satisfied customers are the bank’s best sales force, and if they generate trust for the bank, they
will give referrals to other people. Moreover, new customers who come to the bank through
Figure 1 - Explaining customer behavior (adapted from Klaus, 2015)
13
referral are usually more loyal than those that come for other reasons (Goodwin and Gremler,
1996). McDougall and Levesque (2000) proves the effect of the value expected by the
customers on their loyalty intentions, i.e., customers become loyal to the bank because they
expect to receive value in exchange.
2.5. Research Hypotheses
According to the two studies of this dissertation, the research hypotheses are divided. The first
study about comparing the customer value and its dimensions between the two types of banks,
has the following research hypothesis:
2.5.1. Which is the type of bank that provides more value to its customers?
In this part of the first study, a scale of customer value with its factors is build, tested and
validated, in order to have a measurement model that allows the comparison of the overall score
of customer value between the two types of banks: Challenger banks and Traditional banks.
Using questionnaire as the method of primary data collection and Confirmatory Factor Analysis
to estimate the factor scores from the answers of the customers from both type of banks and
combine those factors to estimate the overall customer score and conduct further analysis
(second study). After reviewing the literature, it’s hypothesized:
H1: Overall customer value is scored higher for Challenger banks than for Traditional banks.
2.5.2. Which dimensions are scored higher and lower for each type of bank?
In this part of the first study, using the results from the questionnaire, the factor scores are
compared for both type of banks in order to understand where each type of bank has higher and
lower values. This is done using the factor scores estimated with CFA. After reviewing the
literature, it’s hypothesized:
H2: The factors Peace of mind, Moments-of-truth, Outcome focus, Price offer fairness, Price
transparency, Social Value are scored higher for Challenger banks than Traditional banks.
H3: The factors Product experience and Emotional Value are scores higher for Traditional
banks than Challenger banks.
The second study about the effect of customer value on consumer behavior outcomes from
literature (WOM and LOY), has the following research hypothesis:
14
2.5.3. Which dimensions of customer value have greater effect on WOM and
LOY for each type of bank?
In the second study, using the estimated factor scores of customer value factors and doing
further estimation of WOM and LOT factor scores, the Structural Equation Modelling (SEM)
is applied to get the effects that each factor of customer value has on those consumer behavior
outcomes from literature. This will enable the comparison between both effects in order to
conclude which factor of customer value predicts better consumer behavior outcomes. Figure
2 illustrates the second study.
Figure 2 – Second study SEM conceptualization with a) representing the isolated effects that each factor has on
WOM and b), the isolated effects that each factor has on LOY.
After reviewing the literature, it’s hypothesized:
H4: Customer value has a positive effect on WOM and LOY, being a good predictor of
consumer behavior outcomes.
H5: The factors of customer value have positive effects on WOM and LOY, being also, in part,
predictors of consumer behavior outcomes.
Factors of customer value
a) EXQ (includes four factors)
b) Price offer fairness
c) Price transparency
d) Social value
e) Emotional value
Word-of-Mouth
Behavior Loyalty Intentions
a)
b)
15
3. METHODOLOGY
3.1. Methodology synthesis
In order to answer the research questions, the method of primary data collection selected was a
questionnaire. From a review of academic articles and practice literature, a 46-item
questionnaire was generated, with a total of ten factors. Figure 3 summarizes the references
used for each dimension.
Dimensions References
Peace of mind, Moments-of-truth, Outcome focus
and Product experience
Klaus and Maklan (2011)
Price offer fairness Herrmann (2007)
Price transparency Stormer (2004)
Social value and Emotional value Roig et al. (2006) and Ivanauskiene et al. (2012)
Word-of-mouth Brown et al. (2005)
Behavior Loyalty Intentions Zeithmal et al. (1996) and Parasuraman et al. (2005)
Figure 3 - Selected Articles of Previous Studies for this dissertations
For the first study, a customer value scale is built after the pilot-test and validation phases, with
34-items and eight factors: Peace of mind, Moments-of-truth, Outcome focus and Product
experience, Price offer fairness, Price transparency, Social Value and Emotional Value. CFA is
used to estimate the factor scores and estimate the overall customer value score. The second
study about the effect of customer value and its factors on consumer behavior outcomes was
performed using SEM as the main method.
3.2. Survey sampling
The questionnaire was carried out in May and June of 2018 and a total of 365 individuals
responded to the online survey, validly surveying 201 customers of banks located in UK.
With a completion rate of 100% there are 163 valid surveys. For this research’ purposes, not
only the surveys with a completion rate of 100% were considered, but also the ones with lower
completion rates that can contribute for the analysis. Figure 4 presents what is the missing data
in each group of surveys according to the completion rate.
16
Completion Rate # Surveys Description of missing data
100% 163 No missing Data.
68% - 46% 21
Replied to section 1, section 2 and section 3 of the survey, however, section
3 is not complete. It’s missing the data about Challenger banks for Q4 and
Q5, having only completed the part about Traditional banks and being client
of both type of banks. Moreover, section 4 with demographic data is also
missing. This part of sample can be used for first and second studies.
37% – 36% 17
Replied to section 1, section 2 and regarding section 3 they only replied until
Q4. Q5 that captures the Consumer Behavior part is missing. Moreover,
section 4 with demographic data is also missing. This part of sample can only
be used for the first study.
< 36% or errors 164
164 surveys were deleted because of one of the following reasons: non-bank
clients are excluded (7 respondents were in this situation), completion rate is
below 36%, the respondent is neither living in UK or from UK (has to be in
one of this situations), surveys with mistakes in the answers (e.g. because of
not understanding the question, an individual responded to the part of
Traditional bank thinking about the experience that had with both type of
banks simultaneously, which should be considered separated).
Figure 4 - Completion Rates and Missing Data explanation
For the first study, the deletion of missing data resulted in 201 valid responses which includes
three possibilities, as it’s described in Figure 5. This means that 28% of the whole sample
represent individuals that are clients of both type of banks. For the second study, there are 177
valid responses.
Studies Total sample Traditional bank only Challenger bank only Both
First study respondents 201 144 8 49
Second study respondents 177 120 8 49
Figure 5 - Number of respondents for each study divided in three different possibilities: being client both of
Traditional banks and Challenger banks, only of Traditional banks or only of Challenger banks-
Besides having missing data in certain parts of the questionnaire, the sample also has missing
values in some of the respondents’ answers. In order to estimate factor scores, data imputation
must be done and, in order to do so, there cannot exist missing values in the data. The
17
questionnaire had the option Do not know/Not Applicable, which results in missing values. For
this reason, missing values had to be estimated through Expectation Maximization method in
SPSS, which has as assumption that the missing values are completely random which was
assessed with Little’s Missing Completely at Random (MCAR) test (Roderick J. A. Little,
1988).
There are some reasons behind the choice of selecting UK banks. UK has a FinTech adoption
index of 42% which includes FinTech users as a percentage of the digitally active population
(EY, 2017), being the highest rate among developed markets and the third country after China
(69%) and India (52%). The term “Challenger bank” was originated in UK, existing sixty-four
Challenger banks there according to Mapa Research (January, 2018) and the vast majority of
them are based in UK due to several factors such as friendly regulatory environment and the
strong entrepreneurial FinTech ecosystem. The Financial Conduct Authority and the Prudential
Regulation Authority have approved multiple FinTech Startups for banking licenses, i.e., that
wanted to become fully licensed banks.
3.3. Data collection
For data collection, several sampling methods were employed. The survey was published in
Reedit11 in several groups which allowed the discussion about the thesis topic with people living
in UK. Besides that, Facebook groups of people such as student’s clubs from UK Universities,
immigrant and emigrant groups of people that still had a bank account in UK, FinTech groups,
academic research groups, and other Facebook groups. Furthermore, Twitter was also used to
contact directly followers of Challenger banks to increase the number of customers that are
users of these banks. These followers were contacted one by one which also allowed discussions
and exchange of knowledge and experiences with banks in UK. However, not only followers
were contacted, but also, people that reached these Challenger banks complaining or praising
about them or simply asking questions regarding the app, which allows the sample to have also
customers that experienced customer support from them.
11 Reedit is a social news aggregation, web content rating, and discussion website.
18
3.4. Descriptive statistics of Data
The sample appears to be representative of banking customers in UK (Figure 6).
Demographic Data Frequency Valid Percentage
Age
18 - 25 38 23,3%
26 - 35 54 33,1%
36 - 45 32 19,6%
46 - 55 21 12,9%
56 - 64 12 7,4%
65+ 6 3,7%
Gender
Male 75 46,0%
Female 86 52,8%
Prefer not to say 2 1,2%
Highest level of education
No schooling completed 2 1,2%
High school graduate or diploma or equivalence 15 9,2%
Some college credit, no degree 10 6,1%
Trade/technical/vocational training 12 7,4%
Associate degree 4 2,5%
Bachelor’s degree 58 35,6%
Master’s degree 38 23,3%
Professional degree 10 6,1%
Doctorate degree 12 7,4%
Other 2 1,2%
Employment Status
Employed for wages 69 42,3%
Self-employed 34 20,9%
Unemployed 5 3,1%
A homemaker 2 1,2%
A student 33 20,2%
Retired 12 7,4%
Unable to work 2 1,2%
Working student 6 3,7%
Total valid 163 100,0%
Missing 46
Total 209
Figure 6- Descriptive Statistics: Demographic Data of respondents
19
Other data such as the Traditional and Challenger banks used by the respondents and for how
long the respondents were clients of those banks is reported in Appendix 4.
3.5. Scale development and validation
The Stage 1 included searching for already validated methodologies that measure customer
value and its dimensions or factors and the Stage 2 involved the beginning of the validation
process. The following stages are described in Figure 7 and afterwards in more detail.
Stage 3: Pilot-testing and final version of survey
The stage 3 included a pre-test that was carried out with a group consisting of twenty bank
customers, and the results permitted to consider the questionnaire definitive. According to
Cronbach’s Alpha’ criterion, the reliability assessment of each dimension ranged from 0.70 to
0.88 excluding the dimensions of EXQ which presented a CA of 0.46, on average, which will
be ignored since EXQ scale has been reliable and validated in multiple contexts, including in
retail banking industry (Figure 8).
Stage 4:
Reliability and
validity
assessment of
measurements
Several factors were
taken into consideration:
- Insights from literature
review were used
- Methods that were
already applied and
validated with retail
banking customers are
more reliable
- The more applicable are
the survey questions to
the banking industry the
better
- Opinions of contacted
experts in FinTech
area, for example, the
CEO and Research
Director of a Startup in
UK that does research
about FinTech, were
taken into account
regarding survey
questions
- Reviews of survey
with experts in
customer value and
customer experience
measurement
- Survey was initially
tested and validated in
terms of reliability
with Cronbach’s
Alpha
- Factors with a
Cronbach’s Alpha
higher than 0.70 are
consider to have an
“acceptable” internal
consistency in most
social science research
and those were kept
- Constructors with
less than 0.70 were
excluded from the
survey
- Final version of
survey
Stage 1: Select the most
appropriate methods that
make sense in Banking
industry to build an
initial survey
Stage 2: Face validity
which included talking
with experts in areas
of interest for this
analysis
Stage 3: Pilot test in
terms of reliability
regarding each factor of
customer value and final
version of survey
- Reliability test with
the final dataset
- Confirmatory
Factor Analysis
(CFA)
- Computation of
Factor Loadings and
test for convergent
validity
- Test for tau-
equivalence: chi-
square difference
between two models
with different and
equal weighting
Figure 7 – Scale development
20
Scale, dimensions or factors Cronbach’s Alpha
Peace of mind, Moments-of-truth, Outcome focus and Product experience 0.46
Price offer fairness 0.78
Price transparency 0.88
Social value 0.70
Emotional value 0.87
Word-of-mouth 0.83
Behavior Loyalty Intentions 0.88
Figure 8 - Internal Consistency of measurements with dataset of Pilot-testing
All the scales measuring the factors/dimensions have been validated in previous studies and
were adapted to ensure applicability in the context of banking. After stage 3 with the pilot-test,
in order to measure customer value, 34 items and 8 dimensions remained, namely:
(1) Peace of mind (PEA). This factor describes the customer’s assessment of all the
interactions with the bank before, during and after dealing with it, being about building
a relationship with the bank. It includes emotional aspects of the service regarding the
benefits experienced based on the perceived expertise of the bank and direction given
during the process, which should be easy and increases confidence.
(2) Moments-of-truth (MOM). This factor is based on literature about service recovery and
flexibility. Describes the influence of the bank behavior on a current or future decision
in case of a mishap, incorporating aspects such as interpersonal skills and perception of
risk in case a situation happens.
(3) Outcome focus (OUT). This factor is about having a bank that reduces the transaction
cost faced by the customers (seeking out and qualifying new providers) and that
provides goal-oriented experiences to their customers, which are seen as a strong basis
for the customer to build a habit of using that bank despite the awareness of other
offerings and the competitiveness of the bank.
(4) Product experience (PRO). This factor represents choice dynamics (McAlister and
Srivastava, 1991), i.e., the customers’ perception of having choices and the ability to do
comparison of offerings within the same bank.
(5) Price offer fairness (POF). This factor was developed by Herrmann (2007) in the
context of automobile purchases. It represents a perception of the customer on the
equality of treatment across customers, the degree that customers perceive that a cost-
21
based pricing strategy is performed by the bank and the customers’ perceptions of the
relationship between their needs and the price.
(6) Price transparency (PTR). This factor was developed by Störmer (2014) in motor
insurance context and was designated as the satisfaction with perceived price
transparency. It was adapted to banking context and it’s composed by four items.
(7) Social value (SOV). This factor was developed by Roig et al. (2006) and Ivanauskiene
et al (2012). It is related with the social impact of the service purchase made by the
customer and includes the social benefits resulting from establishing a relationship with
the bank. Five items were selected to represent social value.
(8) Emotional Value (EMV). This dimension was developed by Roig et al. (2006) and
Ivanauskiene et al (2012). Emotional Value consists of the feelings such as positive
atmosphere, relaxation, trust, confidence and happiness, generated by the experience
with the bank.
Furthermore, it was included in the questionnaire two dimensions of Consumer Behavior with
a total of 12 items, namely:
(1) Word-of-mouth Behavior (WOM). The scale used for this factor is the one developed by
Brown et al. (2005) that considers WoM as an informal communication between two
people: a communicator that is perceived as noncommercial and a receiver, about a
target object (e.g. bank’s brand, product or service) transferred via some communication
medium.
(2) Behavior Loyalty Intentions (LOY). The scale used for this factor is the one developed
by Zeithaml et al. (1996) and Parasuraman et al. (2005) and intends to represent the
factor loyalty that the customers have with their bank. All the items of Behavior Loyalty
Intentions were considered.
The questionnaire structure is represented in Appendix 5 and its questions in Appendix 6. The
questionnaire is in English. The third section of the questionnaire is intended to define how the
respondents qualify the selected customer value dimensions, based on a 7-point Likert type
response scale being the most negative description presented in the left side and the most
positive in the right side (“1 – Strongly Disagree” to “7 – Strongly Agree”), having the Do not
know/Not Applicable option in the extreme right side. The seven-level Likert scale is used
questionnaire-wide and ensures constancy in data collection and evaluation. The last and fourth
section enabled to explore demographic characteristics of the respondents.
22
Stage 4: Reliability and validity assessment of measurements
The first step to do scale refinement is the computation of coefficient α, i.e., Cronbach Alpha
(Churchill, 1979). The reliability test with α was performed a second time with the final dataset
for all factors of customer value, and the values ranged from 0.81 to 0.95, excluding Price offer
fairness with a lower value of 0,57, as expressed in Figure 9. The reliability of the instrument
contributes to its validity. According to Nunnally’s criterion, values above 0.80 indicates high
reliability and the minimum satisfactory value is 0.70 it’s considered “acceptable”. In this case
the items above 0.80 part of the questionnaire will most likely be measuring what is proposed
to measure.
Scales, dimensions or factors Cronbach’s Alpha
Peace of mind, Moments-of-truth, Outcome focus and Product
experience
0.81
Price offer fairness 0.57
Price transparency 0.89
Social value 0.81
Emotional value 0.90
Customer Value Scale 0.92
Word-of-mouth 0.95
Behavior Loyalty Intentions 0.91
Figure 9 – Internal Consistency of measurements with final dataset
In order to have a Cronbach’s Alpha indicator per factor, this indicator is a weighted average
by the number of respondents, of both type of bank’ samples. After the reliability test, CFA was
performed using the AMOS 22.0 program, an added SPSS module for SEM and CFA. Although
it’s common in scale development, Exploratory Factor Analysis (EFA) was not considered for
this research because EFA’s purpose is to discover the latent factors without substantive
constrains on the Data and assuming that all items load on all factors. On the contrary, CFA is
theory-driven, i.e., tests if the Data fits a hypothesized measurement model being applied when
there is some information available about the underlying latent variable structure (i.e., customer
value’s structure) which is the case of this research, having the factors of customer value based
23
on literature. The same happens for the consumer behavior outcomes, WOM and LOY are both
constructs that already exist in literature.
The output of CFA gives a global model fit test, the significance of item loadings and the factor
loadings themselves, among other indicators. Also, it’s possible to test appropriateness of model
constraints or model additions via tests for change in model fit. After estimating the CFA, the
next step is to assess how well the model matches the observed data.
The maximum likelihood estimation method (MLE) was used to perform CFA. Fabrigar,
Wegener, MacCallum and Strahan (1999) argued that MLE is the best choice when data is
relatively normally distributed because it allows to calculate a wide range of measures of the
goodness of fit of the model and the significance testing of the factor loadings which represent
the independent contribution of each item to the factor, i.e., it’s the correlation between the
observed score and the latent score. In order to use this method, the normality assumption had
to be examined for each variable in the proposed model (Hair et al., 2010). The values of the
all variables (items) for univariate skewness and kurtosis were acceptably within the criteria for
normality, which is -3 to 3 for skewness and -10 to 10 for kurtosis (Kline,2006).
3.6. Measurement models
Model 1 - First-order 8-factor model with 34-items
For the factor construction, i.e., to assign scores for individual responses to each question
(item), it must be decided between the assignment of equal or different weights to each item. In
scale construction this appears to be an open question. Babbie (2007) suggests that there should
be given equal weights to items “unless there are compelling reasons for differential weighting”
because if not, “equal weighting should be the norm”. Babbie (2007) states that it has to be
done a validation of the scale through item analysis to examine the extent to which the
composite factors are related to the individual and respective items included in that factors, i.e.,
providing a test of the independent contribution of each item to the factor, in order to select the
best items for the scale, which is done through CFA.
In order to compare both methods and conclude about which to follow, a new measurement
model was created by adding a constrain to the previous model: factor loadings are fixed to 1,
i.e., assumes equal weight of each item in their respective factor. The differences in both models
fit are compared through a tau-difference test, also known as a χ2 difference test, a test used to
24
compare and evaluate an adequate model and other alternative measurement models. The
difference of the χ2 values and the difference of the degrees of freedom are taken and, the χ2
difference is significant, which means that the “larger” model with different weighting of factor
loadings fits the data better than the model in which the factor loadings are fixed to one. With
this results it was decided that the best option is to estimate the respective parameters with CFA
and to prefer the model with different weighting (Schermelleh-Engel, K., Moosbrugger, H.,
& Müller, H., 2003).
Model 2 - Second-order model for Customer Value scale with 8 factors
The factor scores were estimated based on the factor loadings with the data imputation tool in
AMOS 22.0. With this factor scores for each factor, a second-order model has to be created in
order to assess the overall customer value scale, being customer value a latent variable and the
factors composite variables of their items that are observed variables. It is clear that the factors
load into a latent variable (Figure 9). The factor loadings from the sample of Traditional banks
is more reliable due to the bigger size of the sample.
Figure 9: 2nd Order CFA for second-order model of customer value scale with 8 factors, for both samples of
Traditional bank (Customer_value_t) and Challenger banks (Customer_value_c). Numbers represent factor
loadings.
In order to make both customer value scores comparable across two samples with different
sizes, allowing clear interpretation, the scores of the customer value scale were computed
assuming equal weighting of the factors. Research has suggested that this solution can be more
reliable in some cases compared with more complex approaches. Having samples with different
sizes and factor loadings that are different for each, summing scores is more effective (Grice,
25
2001). Furthermore, with the reliability analysis using Cronbach’s Alpha, knowing that this
indicator assumes equal weight for each factor, the result was 0.92 regarding factors of customer
value, which is an excellent indicator of reliability for this method with equal weighting.
Comrey & Lee (1992) suggests that a simple way to estimate factor scores involves summing
the scores corresponding to all items loading on a factor using simple (0,1) weighting, i.e., if an
item loads on a factor a weight of one should be given to it and zero weight if it does not load.
By summing the individual factor scores per response, the customer value per individual is
obtained. Then, to assess the overall customer value, an average of the individuals’ customer
value is computed, considering the size of the samples.
Factor scores estimated by AMOS are a function of the items scores and their respective weights
and Customer value scale for each individual is a sum of the respective factor scores.
Models 3.1 and 3.2 - Factor Analysis for WoM and LOY
Estimating the extent to which each factor of customer value explains consumer behavior and
the customer value itself as a predictor of consumer behavior for each type of bank, is part of
the second study and uses SEM as main the method. Factor analysis was also performed to
estimate the factor scores of WOM and LOY (Figure 10). The same was done for the sample
of Challenger banks.
Figure 10 – Word-of-Mouth (WoM) and Loyalty Behavior (LOY) factors and measurement models
26
Model 4 and 5 – Effect on Consumer Behavior Outcomes: WoM & LOY
In order to assess the effect of overall customer value construct on consumer behavior
outcomes, the structural model expressed in Figure 11 aims to measure that effect using the
SEM methodology. Moreover, it’s also important to study the individual effect of each
customer value’ factor and its significance on consumer behavior outcomes in order to validate
that customer value’ factors are reflected into marketing outcomes that are relevant for banks.
These two models were applied for each factor which means there is a total of sixteen
measurement models (Figure 12).
27
Figure 11 – Structural Model 4 with Customer Value construct and its effect on LOY and WOM
Figure 12 - Structural Model 5 example with Emotional Value factor (EMV) and its effect on LOY
28
4. RESULTS
4.1. Results from the first study
4.1.1. Scale validation
With the results obtained from validation it’s possible to conclude that the 34-items customer
value scale has a multidimensional character, formed by eight factors: PEA, MOM, OUT, PRO,
PEA, PTR, SOV and EMV. The scale reflects internal consistency, remains consistent across
two different samples and surpasses the reliability and validity tests performed. After validating
the customer value scale with a first-order model of the factors and a second-order model in
which customer value is a construct of those factors, it’s possible to analyze the results that this
scale provides with both samples of customers from two types of banks.
In order to advance with the first study, the measurement first and second-order models have
to be validated with CFA with the data from the sample of Traditional bank users (N=201). In
SEM, to assess convergent validity, the maximum likelihood loading of each item has to be
significant to its underlying construct (Arnold and Reynolds, 2003). In this research, all factor
loadings for items measuring the same factor were statistically significant, which reflects that
all items successfully measure their corresponding factor (Anderson and Gerbing, 1988).
Moreover, the factor loadings obtained are higher than 0.4 being the lowest value 0.44 and
higher 0.93 (Figure 13). The factor loadings were computed also for the sample of Challenger
banks users which confirmed, once more, the convergent validity and relevance of the items for
those corresponding factors.
The fit of the measurement models examined was assessed with several indices, which is
recommended by Hoyle and Panter (1995). The results revealed a good model fit. Chi-squared
is significant with p-value = 0, the χ2 /df less than 3 is considered a good fit (Kline, 2006) and
the accepted level for the RMSEA indicator is less than 0.10, which indicates a good model fit
(Hair et al., 1998, p. 772). NFI and CFI have a recommended threshold of > 0.90 that was
adopted as indicative of good model fit for these indices. With this results it’s possible to
conclude that the measurement models of first and second-order have a good fit.
29
Items Factor Loadings
1 2 3 4 5 6 7 8
T C T C T C T C T C T C T C T C
PEA1 0.82 0.81
PEA2 0.80 0.69
PEA3 0.73 0.86
PEA4 0.44 0.79
PEA5 0.73 0.78
PEA6 0.68 0.86
MOM1 0.73 0.90
MOM2 0.67 0.79
MOM3 0.65 0.77
MOM4 0.75 0.91
MOM5 0.78 0.91
OUT1 0.74 0.79
OUT2 0.83 0.83
OUT3 0.74 0.79
OUT4 0.69 0.88
PRO1 0.63 0.69
PRO2 0.46 0.73
PRO3 0.94 0.83
POF1 0.94 0.98
POF2 0.46 0.54
POF3 0.38 0.68
PTR1 0.84 0.92
PTR2 0.87 0.86
PTR3 0.84 0.67
PTR4 0.69 0.88
SOV1 0.81 0.84
SOV2 0.70 1.00
SOV3 0.79 0.73
SOV4 0.59 0.59
SOV5 0.56 0.66
EMV1 0.81 0.93
EMV2 0.85 0.90
EMV3 0.80 0.88
EMV4 0.81 0.85
Figure 13 – Results of CFA for the 1st order 8-factor model (with p-value = 0.00 < 0.05), being T referred to
Traditional banks and C for Challenger banks
30
4.1.2. Descriptive statistics of items
The descriptive statistics for each variable or item are presented in Figure 14. On average,
customers have more confidence and trust in their Traditional bank than in their Challenger
bank (PEA1 and EMV3). Moreover, customers of Traditional banks strongly agree that their
bank is very safe and reputable while Challenger banks’ customers agree with that but gave it
a lower average score, on average (MOM3). Although Traditional banks are perceived as more
trustable, safe and reputable, Challenger banks win in terms of price transparency, having
higher average scores in all items. In terms of process ease, Challenger banks are easier to deal
with (PEA2) and their customers stay with their bank because of past dealings with other banks,
while with Traditional bank users that is not true, i.e., the convenience retention is positive and
higher for Challenger banks (PEA4). Both type of banks had their customers feeling familiar
with them, having the same average score (PEA5). Challenger banks are perceived as being
more flexible and pro-active in keeping their customers up to date compared with Traditional
banks (MOM1 and MOM2). Regarding social value, both Traditional banks and Challenger
banks are perceived by their customer has being well considered at a social level, however,
Traditional banks have a higher score in this case (SOV1). Challenger banks users agree, on
average, that being customer of a Challenger bank looks good to the people that they know
which is not the case for Traditional bank customers (SOV2). Except for trust, other emotional
value’ items such as positive atmosphere, relaxation and happiness have higher scores for
Challenger bank’ users.
Items (34) Factors of Customer value
Traditional banks Challenger banks
Mean Std.
Deviations Mean
Std.
Deviations
Peace-of-mind
PEA1 I am confident in this bank’s expertise. 5.22 1.52 4.97 1.85
PEA2 The whole process of banking is easy. 5.08 1.66 5.75 1.72
PEA3 This bank will look after me for a long time. 4.66 1.768 4.62 1.99
PEA4 I stay with this bank because of my past dealings with other
banks. 3.71 2.05 4.97 1.88
PEA5 I have dealt with this bank before so getting what I need is
really easy. 5.03 1.57 5.03 1.99
PEA6 This bank provides an independent advice. 4.39 1.72 4.47 2.13
Moments-of-truth
MOM1 This bank is flexible in dealing with me and looking after
my needs. 4.57 1.69 4.91 1.99
MOM2 This bank keeps me up to date. 5.16 1.60 5.28 1.90
31
MOM3 This bank is safe and reputable. 5.73 1.46 4.74 1.80
MOM4 The employees of this bank have good people skills. 5.06 1.50 5.07 2.04
MOM5 This bank deal(t) with me correctly when things go (went)
wrong. 5.12 1.54 5.1 2.02
Outcome focus
OUT1 Staying with this bank makes the process much easier. 5.36 1.53 5.24 1.89
OUT2 This bank gives me what I need, swiftly. 5.04 1.56 5.46 1.69
OUT3 I prefer this bank over an alternative provider. 4.83 1.68 5.17 1.80
OUT4 The people at this bank can relate to my situation. 4.43 1.58 4.57 1.86
Product Experience
PRO1 I need to choose between different options at this bank, to
make sure I get the best offer. 4.45 1.61 4.04 2.02
PRO2 I need to receive offers from more banks than just this
bank. 3.55 1.99 4.19 2.06
PRO3 I need to compare different options from this bank, to
know which one is the best for me. 4.59 1.65 4.31 2.08
Price Offer Fairness
POF1 All customers are treated equally by the bank’s pricing. 4.31 1.60 5.65 1.55
POF2 I think the prices of the bank’s services are based on its
costs. 3.99 1.52 4.95 1.80
POF3 The price of the bank’s services are independent of
customer’s needs. 4.43 1.37 4.74 2.00
Price Transparency
PTR1 The presentation of this bank price composition is
complete and correct. 4.9 1.47 5.5 1.56
PTR2 The presentation of this bank price composition is clear
and understandable. 4.8 1.59 5.57 1.54
PTR3 I have a clear overview about the costs of this bank
services. 4.67 1.66 5.65 1.54
PTR4 I know what I have to pay and what I get. 5.33 1.58 5.94 1.35
Social Value
SOV1 This bank is very well considered at a social level. 4.89 1.59 4.6 1.89
SOV2 The fact that I am user of this bank looks good to the people
that I know. 3.94 1.71 4.84 1.86
SOV3 This bank strives to establish long-term relationship with
customers 4.72 1.75 4.72 2.01
SOV4 My relatives, friends and/or acquaintances recommend me
this bank. 4.31 1.96 4.35 2.15
SOV5 When choosing this bank’s services I follow my personal
confidence. 4.95 1.51 5.37 1.70
Emotional Value
32
EMV1 This bank creates a positive atmosphere. 5.01 1.52 5.44 1.70
EMV2 Being client of this bank makes me feel relaxed. 4.84 1.56 5.12 1.81
EMV3 I feel trust and confidence in this bank. 5.34 1.51 4.86 1.71
EMV4 I am happy with the financial services contracted. 5.01 1.55 5.48 1.64
4.1.3. Estimated factor scores (model 1)
Factor scores were estimated for each factor (Figure 15).
Factors of Customer value
Traditional banks Challenger banks
Mean Standard
Deviations Mean
Standard
Deviations
Peace-of-mind (PEA) 3.70 0.88 4.72 1.45
Moments-of-truth (MOM) 4.51 1.08 4.96 1.8
Outcome focus (OUT) 4.17 1.05 4.84 1.59
Product Experience (PRO) 4.44 1.46 3.94 1.58
Price Offer Fairness (POF) 1.41 0.49 4.87 1.33
Price Transparency (PTR) 3.62 1.03 4.76 1.14
Social Value (SOV) 2.70 0.78 2.94 1.12
Emotional Value (EMV) 4.44 1.17 4.48 1.35
Figure 15 – Means and standard deviations for the factor scores obtained from the 1st order 8-factor model
When looking at the mean scores and standard deviations, the average scores of the several
factors range from 2.94 to 4.96 for Challenger banks and 1.41 to 4.51 for Traditional banks,
being 1 the lowest possible value, and 7 the higher and the best. This results show that the eight
dimensions have a hierarchical order for customer value. For both Traditional and Challenger
banks, the factor MOM, leads the way with a mean score of 4.51 and 4.96 respectively. The
lowest mean score is POF for Traditional banks, which is 1.41, a very low value which reflects
that customers strongly disagree, on average, that their bank’s prices are fair, while for
Challenger banks this mean score is much higher, being 4.87. Furthermore, SOV is the lower
factor mean score for Challenger banks, which is even lower for Traditional banks, which
means that the social benefits resulting from establishing a relationship with the bank are very
low for both. Regarding the other factors part of EXQ methodology, such as PEA, OUT and
PRO, Challenger banks present higher scores in the four factors (including MOM). The PEA
factor is 4.72 for Challenger banks which is above the medium value 4 reflecting a positive
Figure 14 – Means and standard deviations of items for Traditional banks (N=201) and Challenger
banks (N=57) samples
33
assessment from customers regarding the relationship stablished with the bank, before, during
and after dealing with the bank. For Traditional banks, this result is not a good indicator of
PEA. The factor OUT, reflect both mean scores above 4 and higher for Challenger banks. About
the factor PRO, it’s the only factor that presents a higher mean score for Traditional banks
(4.44) compared with Challenger banks (3.94). Traditional banks provide a higher feeling of
“having a choice” and just because of that they are more likely to accept the offer (McAlister
and Srivastava, 1991), compared to Challenger banks. Moreover, Challenger banks provide
higher satisfaction regarding perceived price transparency. When it comes to EMV, both bank
present a similar score above 4, except regarding trust (EMV3) which is higher for Traditional
banks.
4.1.4. Overall customer value score (model 2)
The results of factor loadings for the second-order model (Figure 9 in methodology section)
demonstrate that out of eight factors, seven factors appear to be very significant for customer
value, among which MOM and PEA appear to be the most important factor for Traditional
banks. For Challenger banks, PEA and OUT seem to be the most important. Having customer
value scale eight factors and knowing that each one can have a maximum score of seven points,
the best-case scenario would be a maximum score of fifty-six points. Challenger banks have
the higher overall customer value score (Figure 16).
Customer Value Traditional Banks Challenger Banks
CV (sum of scores) 28.98 35.52
CV (% of maximum value) 51.75% 63.43%
Figure 16- Customer value overall score as a sum of individual scores for each factor and as a percentage of
maximum possible score of customer value
4.2. Results from the second study
4.2.1. Models validation
The fit of the measurement models 3, 4 and 5, part of the second study was also assessed and
the results revealed an acceptable model fit. In order to proceed to study the effect that customer
value and its factors have on consumer behavior outcomes (WOM and LOY), it’s relevant to
34
perform factor analysis for WOM and LOY models isolated from each other. After the
estimation of factor loadings, it was also possible to conclude that there’s convergent validity
with all factors loadings being significant and higher than 0.40 (Figure 17).
Figure 17 – CFA results of WOM and LOY measurement models 3.1 and 3.2 with numbers representing the factor
loadings (with p-value = 0.00 < 0.05) for Traditional banks (N=177) and Challenger banks (N=57) samples
4.2.2. Descriptive statistics of items
Results from the descriptive statistic of WOM and LOY (Figure 18) show that, on average,
customers of Challenger banks mention and make sure that other know they make business with
their bank (WOM1 and WOM2), adding the fact that they recommend their bank (WOM4,
WOM6 and WOM7). Moreover, they speak positively of their bank to others (WOM5) and
about the bank’ employee(s) (WOM3). For Traditional bank users the scenario is different.
Although, on average, they speak positively about their bank employee(s) and the bank in
general, adding the fact that they also do recommendations, they don’t mention, neither make
sure that other know that they do business with their bank, compared with Challenger banks.
Regarding LOY, Challenger banks had better scores than Traditional banks in three items
(LOY1, LOY2 and LOY3) and, lower scores in two items (LOY4 and LOY5).
35
Items
(12) Consumer Behavior Outcomes
Traditional banks Challenger banks
Mean Standard
deviations Mean
Standard
deviations
Word-of-Mouth Behavior (WOM)
WOM1 I mention to others that I do business with this bank. 3.82 1.97 5.01 2.02
WOM2 I make sure that others know that I do business with this bank. 3.24 2.03 4.81 2,00
WOM3 I speak positively about this bank employee(s) to others. 4.34 1.99 4.84 2.1
WOM4 I recommend this bank to family members. 4.18 2.18 5.34 1.88
WOM5 I speak positively of this bank to others. 4.59 1.89 5.3 2.03
WOM6 I recommend this bank to acquaintances. 4.05 2.11 5.27 1.95
WOM7 I recommend this bank to close personal friends. 4.2 2.14 5.43 1.86
Behavior Loyalty Intentions (LOY)
LOY1 I say positive things about this bank to other people. 4.53 1.93 5.41 1.97
LOY2 I recommend this bank to someone who seeks my advice. 4.3 2.07 5.37 2.03
LOY3 I encourage friends and relatives to use this bank. 4.14 2.10 5.41 1.97
LOY4 I consider this bank to be the first choice to use financial
services. 4.43 1.92 4.27 2.08
LOY5 I will use this bank in the next few years. 5.57 1.53 5.45 1.88
Figure 18 - Means and standard deviations for items part of WOM and LOY for Traditional banks (N=177) and
Challenger banks (N=57) samples
4.2.3. Estimated factor scores: WOM and LOY (models 3.1 and 3.2)
Both WOM and LOY are higher for Challenger banks. This results are very positive for both
Challenger and Traditional banks in terms of marketing outcomes being WOM a powerful
outcome (Brown et al., 2005), where the customers communicate the bank existence among
other things to other receivers. Moreover, the customers’ loyalty with the bank is higher for
Challenger banks (5.78) while for Traditional banks is only 3.57, which is not positive, on
average (Figure 19).
Consumer Behavior Outcomes
Traditional banks Challenger banks
Mean Standard Deviations Mean Standard Deviations
Word-of-Mouth (WOM) 4.18 1.87 4.60 1.64
Behavioral Loyalty Intentions (LOY) 3.57 1.47 5.78 4.43
Figure 19 - Means and standard deviations for the factor scores obtained from the measurement models 3.1 and
3.2 for Traditional banks (N=177) and Challenger banks (N=57) samples
36
4.2.4. Effects on consumer behavior outcomes (model 4 and model 5)
The results of the structural model 4 show to which extent customer value explains WOM and
LOY (Figure 20). For Traditional banks, customer value explains 63% of word-of-mouth and
69% of loyalty. For Challenger banks, customer value explains 80% and 85%, respectively.
This validates the notion that customer value assessment goes beyond the direct (service)
encounter.
Figure 20 – Results from SEM of model 4: customer value’s effect on Word-of-Mouth and Loyalty
All of the eight factors have a positive and significant impact on consumers’ behavior (Figure
21), except for MOM in Challenger banks sample. For Traditional banks, MOM, OUT, POF,
SOV and EMV are very good predictors of Word-of-mouth and MOM, OUT, POF, SOV and
EMV are very good predictors of Loyalty. For Challenger banks, all the factors except MOM
are very good predictors of consumer behavior. Investigating the influence of each individual
factor on the outcomes, allows to conclude that POF has the greatest influence on WOM for
both type of banks, adding also PRO for Challenger banks. The factor OUT has the greatest
influence on Loyalty for both, adding also the factor POF for Challenger banks. It’s also
relevant to point out that PTR has not such a great effect on both marketing outcomes for
Traditional banks. However, for Challenger banks, that effect is much higher.
Word-of-Mouth Loyalty
Customer value Traditional banks 0.63 0.69
Customer value Challenger banks 0.80 0.85
37
Traditional banks Challenger banks
Factors
Word-of-Mouth
(WoMt)
Loyalty
(LOYt)
Word-of-Mouth
(WoMt)
Loyalty
(LOYt)
Peace-of-mind (PEA) 0.73*** 0.78*** 0.97*** 0.98***
Moments-of-truth (MOM) 0.97*** 1.01*** -0.24 -0.28*
Outcome focus (OUT) 0.96*** 1.02*** 0.97*** 1.00***
Product Experience (PRO) 0.40*** 0.40*** 0.98*** 0.99***
Price Offer Fairness (POF) 0.99** 0.99** 0.98*** 1.00***
Price Transparency (PTR) 0.45*** 0.47*** 0.88*** 0.98***
Social Value (SOV) 0.97*** 1.01*** 0.99*** 0.98***
Emotional Value (EMV) 0.97*** 1.01*** 0.96*** 0.99***
Notes: Significant at: *0.05, **0.01 and ***0.001 levels; N = 201 for Traditional banks and N = 53 for
Challenger banks
Figure 21 – Results from SEM of model 5: customer value’ factors’ effect on Word-of-Mouth and Loyalty
38
5. CONCLUSION
This study intends to compare customer value across two type of banks by developing a scale
with the intention of measuring customer value having primary data from customers. Moreover,
studies the effect that customer value has on consumer behavior outcomes. The results show
that the study contributes methodologically to existing customer value measurement studies,
applied in financial industry.
From the first study it is possible to conclude that Challenger banks have a stronger customer
value average score compared to Traditional banks which is reflected in customer value factors:
average scores for Peace-of-mind, Moments-of-truth, Outcome focus, Price Offer Fairness,
Price Transparency, Social Value and Emotional Value are higher for Challenger banks and
Product Experience is higher for Traditional banks. Nevertheless, Traditional banks present
higher average scores in some variables such as expertise, risk perception, service recovery,
inertia, freedom of choice, cross-product comparison and trust. Although Challenger banks
have a higher score, both banks are still very far from the maximum score. These conclusions
are aligned with the hypotheses H1, H2 and partly H3, except for Emotional Value factor.
For the second study Word-of-mouth and Behavior Loyalty Intentions were both higher for
Challenger banks, however, the major difference is in LOY, which allows the acceptance of
H4. Moreover, the results from the study of the effects of customer value and its factors on this
consumer behavior show that the effect of customer value and its factors is positive and
significant except for MOM in Challenger banks sample, which means that, those are good
predictors of consumer behavior and once more, allows the acceptance of H5. These findings
suggest the importance of those factors on consumer behavior, validating the notion that
customer value perception has a positive and significant impact on important marketing
outcomes. Furthermore, the factors Price offer fairness and Outcome focus are highly relevant
because of its close link to WOM and LOY for both type of banks.
According to Chis Skinner, author of the daily blog thefinanser.com (2017) there are two
extremes. In one side, we have Traditional Banks centuries of history, a huge customer base
and billions of capital, however, these are stuck in their entrenched legacy. In the other side,
Challenger banks have a new and clear sheet of paper with no history, in most cases not so
many customers and often zero or limited capital, being challenged to build a legacy.
39
This study utilized a convenience sample. The results may have varied if there were more
respondents were also customers of Challenger banks. Further evidence regarding external
validation should be provided by using other samples that include more users of Challenger
banks for future research. Other limitations of this study includes that it’s not easily controlled
if the customers of banks use their main personal bank as their business account as well (which
is quite common on UK), considering that this study focus on the customer value and excludes
B2B. To minimize this risk, in the questionnaire is asked about their main personal bank. Also,
giving the same weight to each customer not considering the period of services’ usage, can be
a limitation so that future research should compare the results obtained with a model that
assumes different weighting. For future research, studying the evolution of customer value and
its factors during time would be relevant since thinking about long-term would be a way to
study if Challenger banks are sustainable as they grow. Moreover, performing cluster analysis
using the respondent’s demographic data to find patterns that relate certain characteristics of
respondents to their perceived value and its factors would also be relevant for literature
considering that FinTech services are being more used by younger and wealthier customers
according to a survey (Holland FinTech, 2015). The early adopters tend to be younger, urban,
tech-savvy and higher-income individuals and millennials constitute a substantial portion of
FinTech use in most countries (Lee et al., 2018).
40
APPENDIX
Appendix 1
Area of activity Description and Examples
Comparison/
Information portals
Websites with a special vertical search engine that focus on a particular segment or product.
Customers find different offers for a wanted product and can compare price and offering. The service
is usually free of charge for the customer. Example: Finanzchef24
Payment FinTech start-ups with focus on payment services that try to change how payments are made in daily
life, e.g. payment via barcode readable by the smartphone, or give their business customers the
option to accept different payment methods. Example: barzahlen
Online
identification
Providers offer a digital verification of customers which is done via a video chat to clearly identify
and verify a person. Verification is required by law for different products and services in the financial
industry. Example: Idnow
Banking Services Banking services offered by Traditional banks, e.g. grating of credit and loans or bank accounts.
Normally, only selected services of the whole banking services portfolio are offered. Offerings
concerning investment, payment and advisory are assigned to separate categories of areas in this
classification. Example: Bringcashnow
Investment and
asset management
Structured and professional management of different securities (e.g. shares) and other assets for
customers. Especially the use of robo-advising is steadily increasing within the area of wealth and
asset management. Example: Ayondo
Advisory Financial advisors are service providers who advise customers on financial products such as
investments, loans and insurances. Example: Rentablo
Insurance Offering of classical insurance services, e.g. sale of product insurance policies (e.g. mobile phone
insurance). Example: Onlineversicherung.de
Intermediaries Intermediaries occupy the interface between the customer and supplier (i.e. mainly banks and
insurance companies). They offer intermediation services for financial products such as loans,
insurance policies, etc. Example: Savedo
Data management FinTech start-ups take over the data management for customers (B2C or B2B). New opportunities
in this area will also result from the PSD2. Example: Simplr
Software solutions FinTech start-ups offer software solutions with different application fields. Software solutions can
be tools for big data analyses, cash register systems incl. payment and accounting tools, digitalization
tools for paper-based documents, etc. Example: Naqoda
Crowd financing Crowd financing as alternative financing method of projects where many investors together fund a
project. Different crowd financing models can be distinguished. Example: Companisto
Blockchain,
cryptocurrency,
Bitcoin
FinTech start-ups that are using or further developing the Blockchain technology in different areas
of application. Example: Bitbond
Others All areas of activities that cannot be allocated to one of the areas above. Examples are butler services
and action platforms.
This table represents the several areas of activity, with the description and examples of FinTech start-ups in those
areas. Even with a clear mapping of FinTech start-ups, some of them could be assigned to more than one area of
41
activity. For this study, only the areas of activity Banking Services and Intermediaries are relevant. Source:
Stuckenborg et al. FinTech start-ups: How do business model, area of activity and revenue model relate.
Appendix 2
This figure represents the service experience construct and measure model (EXQ), one of the
models used to develop customer value scale. Source: Klaus and Maklan (2011)
42
Appendix 3
CATEGORY NEOBANK AND CHALLENGER BANKS
Banking, Retail Banking Bank of Lambeth, VTB
Private Banking Hampden & Co
Banking Service: eMoney Thinkmoney
Banking Service: Lender/Loans City of London Group, PCFG
Banking Service for Freelancers Coconut (Monizo)
Corporate Banking Axis Bank UK, Copernicus Bank, FCMB UK
Current & Savings Account B, Unlon Bank of India (UK) Limited
Current Account Metro Bank
Digital Banking and
stockbroking
Digital Banking Services
Fidor Bank, Lintel Bank, Tandem, FinecoBank
Cashplus, Secco Aura, U (Acount by frees)
Ethical Banking Triodos
Mobile Banking
Mobile Banking Service
Monzo, N26, Starling Bank, Ummah Finance
DiPocket, Loot, Pockit, Revolut, Soldo
Mortgages Amicus, The Services Family
Mortgages and Savings Masthaven, Atom Bank, Secure Trust Bank
Mortgages, Loans and Bridging
Finance Together Money
Payments, Transfers Babb, Curve, FairFX, Monese
Pre-paid Card for Kids goHenry, Osper
Savings Charter Savings Bank, Chip, Community Savings Bank Association, Ford,
Hampshire Community Bank
Savings & Loans Paragon Bank, Shawbrook Bank, ZOPA, Burnley Savings and Loans Ltd
Savings & Loans & Investments OneSavings Bank, Wvelands Bank
Savings & SME Banking Bank of Cyprus UK
SME Banking Cambridge and Countles Bank, Civilised Bank, Countingup, OakNorth,
Redwood Bank
SME Banking Service Tide
SME Banking & Mortgages Aldermore
While label Banking Services Contis Group
This table is adapted from “Challenger Banks in UK” (Mapa Research, January 2018). Mapa Rsearch built a list
of Challenger banks in UK, composed by 62 banks that are organized by the bank name, strapline, category, year
when it was launched, headquarters, description, technology and the stage regarding the banking license. Using
43
that list has a base, this table was created organized per category and reduced to the Challenger banks that focus
on customer services.
Appendix 4
The first figure shows the Traditional Banks in a sample of N=201 respondents and the second
figure, the Challenger Banks in a sample of N=57 respondents.
44
Appendix 5
This figure represents the questionnaire structure composed by four sections. The definitions of
Traditional bank and Challenger bank are presented to clarify and anticipate any confusion with the
terms. Then, with the first section it’s decided if the respondent is part of the target population (users
of banks in UK) and if the answer is affirmative the respondent is categorized into three different
classifications: user of a Traditional bank, user of a Challenger bank or user of both. This way, the
45
user is redirected to the second section according to the option selected. The second section allows to
conclude on which banks are represented in the sample and their market share, the amount of time it
was client of that bank and how would describe the experience that had with that bank in one word.
Appendix 6
Questions Items or output
Q1.1. Have you ever been client of a bank? Yes/No Critical question
Q1.2. Which kind of bank? Traditional, Digital or both
Q1 What is your main personal Traditional/digital bank? Name of the bank
Q2 For how long have you been client of that bank? Period of time
Q3 How would you describe your experience with that bank in one word? Word
Q4_1 I am confident in this bank’s expertise. PEA1
Q4_2 The whole process of banking is easy. PEA2
Q4_3 This bank will look after me for a long time. PEA3
Q4_4 I stay with this bank because of my past dealings with other banks. PEA4
Q4_5 I have dealt with this bank before so getting what I need is really easy. PEA5
Q4_6 This bank provides an independent advice. PEA6
Q4_7 This bank is flexible in dealing with me and looking after my needs. MOM1
Q4_8 This bank keeps me up to date. MOM2
Q4_9 This bank is safe and reputable. MOM3
Q4_10 The employees of this bank have good people skills. MOM4
Q4_11 This bank deal(t) with me correctly when things go (went) wrong. MOM5
Q4_12 Staying with this bank makes the process much easier. OUT1
Q4_13 This bank gives me what I need, swiftly. OUT2
Q4_14 I prefer this bank over an alternative provider. OUT3
Q4_15 The people at this bank can relate to my situation. OUT4
Q4_16 I need to choose between different options at this bank (…). PRO1
Q4_17 I need to receive offers from more banks than just this bank. PRO2
Q4_18 I need to compare different options from this bank (…). PRO3
Q4_19 All customers are treated equally by the bank’s pricing. POF1
Q4_20 I think the prices of the bank’s services are based on its costs. POF2
Q4_21 The price of the bank’s services are independent of customer’s needs. POF3
Q4_22 The presentation of this bank price composition is complete and correct. PTR1
Q4_23 The presentation of this bank price composition is clear and understandable. PTR2
Q4_24 I have a clear overview about the costs of this bank services. PTR3
Q4_25 I know what I have to pay and what I get. PTR4
Q4_26 This bank is very well considered at a social level. SOV1
Q4_27 The fact that I am user of this bank looks good to the people that I know. SOV2
Q4_28 This bank strives to establish long-term relationship with customers SOV3
46
Q4_29 My relatives, friends and/or acquaintances recommend me this bank. SOV4
Q4_30 When choosing this bank’s services I follow my personal confidence. SOV5
Q4_31 This bank creates a positive atmosphere. EMV1
Q4_32 Being client of this bank makes me feel relaxed. EMV2
Q4_33 I feel trust and confidence in this bank. EMV3
Q4_34 I am happy with the financial services contracted. EMV4
Q5_1 I mention to others that I do business with this bank. WOM1
Q5_2 I make sure that others know that I do business with this bank. WOM2
Q5_3 I speak positively about this bank employee(s) to others. WOM3
Q5_4 I recommend this bank to family members. WOM4
Q5_5 I speak positively of this bank to others. WOM5
Q5_6 I recommend this bank to acquaintances. WOM6
Q5_7 I recommend this bank to close personal friends. WOM7
Q5_8 I say positive things about this bank to other people. LOY1
Q5_9 I recommend this bank to someone who seeks my advice. LOY2
Q5_10 I encourage friends and relatives to use this bank. LOY3
Q5_11 I consider this bank to be the first choice to use financial services. LOY4
Q5_12 I will use this bank in the next few years. LOY5
This table presents the questions part of the questionnaire, with the respective question mark
and items that represent each question or variable.
47
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